Made MLlib and examples compile

Author: rxin

examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java

@@ -33,7 +33,7 @@
 import org.apache.spark.ml.tuning.CrossValidator;
 import org.apache.spark.ml.tuning.CrossValidatorModel;
 import org.apache.spark.ml.tuning.ParamGridBuilder;
-import org.apache.spark.sql.SchemaRDD;
+import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.SQLContext;
 import org.apache.spark.sql.Row;
 
@@ -71,7 +71,7 @@ public static void main(String[] args) {
       new LabeledDocument(9L, "a e c l", 0.0),
       new LabeledDocument(10L, "spark compile", 1.0),
       new LabeledDocument(11L, "hadoop software", 0.0));
-    SchemaRDD training = jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class);
+    DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class);
 
     // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
     Tokenizer tokenizer = new Tokenizer()
@@ -112,11 +112,11 @@ public static void main(String[] args) {
       new Document(5L, "l m n"),
       new Document(6L, "mapreduce spark"),
       new Document(7L, "apache hadoop"));
-    SchemaRDD test = jsql.applySchema(jsc.parallelize(localTest), Document.class);
+    DataFrame test = jsql.applySchema(jsc.parallelize(localTest), Document.class);
 
     // Make predictions on test documents. cvModel uses the best model found (lrModel).
-    cvModel.transform(test).registerAsTable("prediction");
-    SchemaRDD predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction");
+    cvModel.transform(test).registerTempTable("prediction");
+    DataFrame predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction");
     for (Row r: predictions.collect()) {
       System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> score=" + r.get(2)
           + ", prediction=" + r.get(3));

examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java

@@ -28,7 +28,7 @@
 import org.apache.spark.ml.classification.LogisticRegression;
 import org.apache.spark.mllib.linalg.Vectors;
 import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.sql.SchemaRDD;
+import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.SQLContext;
 import org.apache.spark.sql.Row;
 
@@ -54,7 +54,7 @@ public static void main(String[] args) {
       new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
       new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
       new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5)));
-    SchemaRDD training = jsql.applySchema(jsc.parallelize(localTraining), LabeledPoint.class);
+    DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledPoint.class);
 
     // Create a LogisticRegression instance.  This instance is an Estimator.
     LogisticRegression lr = new LogisticRegression();
@@ -94,14 +94,14 @@ public static void main(String[] args) {
         new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
         new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
         new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)));
-    SchemaRDD test = jsql.applySchema(jsc.parallelize(localTest), LabeledPoint.class);
+    DataFrame test = jsql.applySchema(jsc.parallelize(localTest), LabeledPoint.class);
 
     // Make predictions on test documents using the Transformer.transform() method.
     // LogisticRegression.transform will only use the 'features' column.
     // Note that model2.transform() outputs a 'probability' column instead of the usual 'score'
     // column since we renamed the lr.scoreCol parameter previously.
-    model2.transform(test).registerAsTable("results");
-    SchemaRDD results =
+    model2.transform(test).registerTempTable("results");
+    DataFrame results =
         jsql.sql("SELECT features, label, probability, prediction FROM results");
     for (Row r: results.collect()) {
       System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2)

examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java

@@ -29,7 +29,7 @@
 import org.apache.spark.ml.classification.LogisticRegression;
 import org.apache.spark.ml.feature.HashingTF;
 import org.apache.spark.ml.feature.Tokenizer;
-import org.apache.spark.sql.SchemaRDD;
+import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.SQLContext;
 import org.apache.spark.sql.Row;
 
@@ -54,7 +54,7 @@ public static void main(String[] args) {
       new LabeledDocument(1L, "b d", 0.0),
       new LabeledDocument(2L, "spark f g h", 1.0),
       new LabeledDocument(3L, "hadoop mapreduce", 0.0));
-    SchemaRDD training = jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class);
+    DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class);
 
     // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
     Tokenizer tokenizer = new Tokenizer()
@@ -79,11 +79,11 @@ public static void main(String[] args) {
       new Document(5L, "l m n"),
       new Document(6L, "mapreduce spark"),
       new Document(7L, "apache hadoop"));
-    SchemaRDD test = jsql.applySchema(jsc.parallelize(localTest), Document.class);
+    DataFrame test = jsql.applySchema(jsc.parallelize(localTest), Document.class);
 
     // Make predictions on test documents.
-    model.transform(test).registerAsTable("prediction");
-    SchemaRDD predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction");
+    model.transform(test).registerTempTable("prediction");
+    DataFrame predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction");
     for (Row r: predictions.collect()) {
       System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> score=" + r.get(2)
           + ", prediction=" + r.get(3));

examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java

@@ -26,9 +26,9 @@
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
 
-import org.apache.spark.sql.SQLContext;
-import org.apache.spark.sql.SchemaRDD;
+import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SQLContext;
 
 public class JavaSparkSQL {
   public static class Person implements Serializable {
@@ -74,11 +74,11 @@ public Person call(String line) {
       });
 
     // Apply a schema to an RDD of Java Beans and register it as a table.
-    SchemaRDD schemaPeople = sqlCtx.applySchema(people, Person.class);
+    DataFrame schemaPeople = sqlCtx.applySchema(people, Person.class);
     schemaPeople.registerTempTable("people");
 
     // SQL can be run over RDDs that have been registered as tables.
-    SchemaRDD teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
+    DataFrame teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
 
     // The results of SQL queries are SchemaRDDs and support all the normal RDD operations.
     // The columns of a row in the result can be accessed by ordinal.
@@ -99,11 +99,11 @@ public String call(Row row) {
     // Read in the parquet file created above.
     // Parquet files are self-describing so the schema is preserved.
     // The result of loading a parquet file is also a JavaSchemaRDD.
-    SchemaRDD parquetFile = sqlCtx.parquetFile("people.parquet");
+    DataFrame parquetFile = sqlCtx.parquetFile("people.parquet");
 
     //Parquet files can also be registered as tables and then used in SQL statements.
     parquetFile.registerTempTable("parquetFile");
-    SchemaRDD teenagers2 =
+    DataFrame teenagers2 =
       sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
     teenagerNames = teenagers2.toJavaRDD().map(new Function<Row, String>() {
       @Override
@@ -120,7 +120,7 @@ public String call(Row row) {
     // The path can be either a single text file or a directory storing text files.
     String path = "examples/src/main/resources/people.json";
     // Create a JavaSchemaRDD from the file(s) pointed by path
-    SchemaRDD peopleFromJsonFile = sqlCtx.jsonFile(path);
+    DataFrame peopleFromJsonFile = sqlCtx.jsonFile(path);
 
     // Because the schema of a JSON dataset is automatically inferred, to write queries,
     // it is better to take a look at what is the schema.
@@ -134,7 +134,7 @@ public String call(Row row) {
     peopleFromJsonFile.registerTempTable("people");
 
     // SQL statements can be run by using the sql methods provided by sqlCtx.
-    SchemaRDD teenagers3 = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
+    DataFrame teenagers3 = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
 
     // The results of SQL queries are JavaSchemaRDDs and support all the normal RDD operations.
     // The columns of a row in the result can be accessed by ordinal.
@@ -151,7 +151,7 @@ public String call(Row row) {
     List<String> jsonData = Arrays.asList(
           "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
     JavaRDD<String> anotherPeopleRDD = ctx.parallelize(jsonData);
-    SchemaRDD peopleFromJsonRDD = sqlCtx.jsonRDD(anotherPeopleRDD.rdd());
+    DataFrame peopleFromJsonRDD = sqlCtx.jsonRDD(anotherPeopleRDD.rdd());
 
     // Take a look at the schema of this new JavaSchemaRDD.
     peopleFromJsonRDD.printSchema();
@@ -164,7 +164,7 @@ public String call(Row row) {
 
     peopleFromJsonRDD.registerTempTable("people2");
 
-    SchemaRDD peopleWithCity = sqlCtx.sql("SELECT name, address.city FROM people2");
+    DataFrame peopleWithCity = sqlCtx.sql("SELECT name, address.city FROM people2");
     List<String> nameAndCity = peopleWithCity.toJavaRDD().map(new Function<Row, String>() {
       @Override
       public String call(Row row) {

examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala

@@ -18,7 +18,6 @@
 package org.apache.spark.examples.ml
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.SparkContext._
 import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
@@ -101,7 +100,7 @@ object CrossValidatorExample {
 
     // Make predictions on test documents. cvModel uses the best model found (lrModel).
     cvModel.transform(test)
-      .select('id, 'text, 'score, 'prediction)
+      .select("id", "text", "score", "prediction")
       .collect()
       .foreach { case Row(id: Long, text: String, score: Double, prediction: Double) =>
       println("(" + id + ", " + text + ") --> score=" + score + ", prediction=" + prediction)

examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala

@@ -18,7 +18,6 @@
 package org.apache.spark.examples.ml
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.SparkContext._
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
@@ -92,7 +91,7 @@ object SimpleParamsExample {
     // Note that model2.transform() outputs a 'probability' column instead of the usual 'score'
     // column since we renamed the lr.scoreCol parameter previously.
     model2.transform(test)
-      .select('features, 'label, 'probability, 'prediction)
+      .select("features", "label", "probability", "prediction")
       .collect()
       .foreach { case Row(features: Vector, label: Double, prob: Double, prediction: Double) =>
         println("(" + features + ", " + label + ") -> prob=" + prob + ", prediction=" + prediction)

examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala

@@ -20,7 +20,6 @@ package org.apache.spark.examples.ml
 import scala.beans.BeanInfo
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.SparkContext._
 import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
@@ -80,7 +79,7 @@ object SimpleTextClassificationPipeline {
 
     // Make predictions on test documents.
     model.transform(test)
-      .select('id, 'text, 'score, 'prediction)
+      .select("id", "text", "score", "prediction")
       .collect()
       .foreach { case Row(id: Long, text: String, score: Double, prediction: Double) =>
         println("(" + id + ", " + text + ") --> score=" + score + ", prediction=" + prediction)

examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala

@@ -28,10 +28,10 @@ import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{Row, SQLContext, SchemaRDD}
+import org.apache.spark.sql.{Row, SQLContext, DataFrame}
 
 /**
- * An example of how to use [[org.apache.spark.sql.SchemaRDD]] as a Dataset for ML. Run with
+ * An example of how to use [[org.apache.spark.sql.DataFrame]] as a Dataset for ML. Run with
  * {{{
  * ./bin/run-example org.apache.spark.examples.mllib.DatasetExample [options]
  * }}}
@@ -81,18 +81,18 @@ object DatasetExample {
     println(s"Loaded ${origData.count()} instances from file: ${params.input}")
 
     // Convert input data to SchemaRDD explicitly.
-    val schemaRDD: SchemaRDD = origData
+    val schemaRDD: DataFrame = origData
     println(s"Inferred schema:\n${schemaRDD.schema.prettyJson}")
     println(s"Converted to SchemaRDD with ${schemaRDD.count()} records")
 
     // Select columns, using implicit conversion to SchemaRDD.
-    val labelsSchemaRDD: SchemaRDD = origData.select('label)
+    val labelsSchemaRDD: DataFrame = origData.select("label")
     val labels: RDD[Double] = labelsSchemaRDD.map { case Row(v: Double) => v }
     val numLabels = labels.count()
     val meanLabel = labels.fold(0.0)(_ + _) / numLabels
     println(s"Selected label column with average value $meanLabel")
 
-    val featuresSchemaRDD: SchemaRDD = origData.select('features)
+    val featuresSchemaRDD: DataFrame = origData.select("features")
     val features: RDD[Vector] = featuresSchemaRDD.map { case Row(v: Vector) => v }
     val featureSummary = features.aggregate(new MultivariateOnlineSummarizer())(
       (summary, feat) => summary.add(feat),
@@ -109,7 +109,7 @@ object DatasetExample {
     val newDataset = sqlContext.parquetFile(outputDir)
 
     println(s"Schema from Parquet: ${newDataset.schema.prettyJson}")
-    val newFeatures = newDataset.select('features).map { case Row(v: Vector) => v }
+    val newFeatures = newDataset.select("features").map { case Row(v: Vector) => v }
     val newFeaturesSummary = newFeatures.aggregate(new MultivariateOnlineSummarizer())(
       (summary, feat) => summary.add(feat),
       (sum1, sum2) => sum1.merge(sum2))

examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala

@@ -19,6 +19,8 @@ package org.apache.spark.examples.sql
 
 import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.dsl._
+import org.apache.spark.sql.dsl.literals._
 
 // One method for defining the schema of an RDD is to make a case class with the desired column
 // names and types.
@@ -54,7 +56,7 @@ object RDDRelation {
     rddFromSql.map(row => s"Key: ${row(0)}, Value: ${row(1)}").collect().foreach(println)
 
     // Queries can also be written using a LINQ-like Scala DSL.
-    rdd.where('key === 1).orderBy('value.asc).select('key).collect().foreach(println)
+    rdd.where($"key" === 1).orderBy($"value".asc).select($"key").collect().foreach(println)
 
     // Write out an RDD as a parquet file.
     rdd.saveAsParquetFile("pair.parquet")
@@ -63,7 +65,7 @@ object RDDRelation {
     val parquetFile = sqlContext.parquetFile("pair.parquet")
 
     // Queries can be run using the DSL on parequet files just like the original RDD.
-    parquetFile.where('key === 1).select('value as 'a).collect().foreach(println)
+    parquetFile.where($"key" === 1).select($"value".as("a")).collect().foreach(println)
 
     // These files can also be registered as tables.
     parquetFile.registerTempTable("parquetFile")

mllib/src/main/scala/org/apache/spark/ml/Estimator.scala

@@ -21,7 +21,7 @@ import scala.annotation.varargs
 
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.param.{ParamMap, ParamPair, Params}
-import org.apache.spark.sql.SchemaRDD
+import org.apache.spark.sql.DataFrame
 
 /**
  * :: AlphaComponent ::
@@ -38,7 +38,7 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params {
    * @return fitted model
    */
   @varargs
-  def fit(dataset: SchemaRDD, paramPairs: ParamPair[_]*): M = {
+  def fit(dataset: DataFrame, paramPairs: ParamPair[_]*): M = {
     val map = new ParamMap().put(paramPairs: _*)
     fit(dataset, map)
   }
@@ -50,7 +50,7 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params {
    * @param paramMap parameter map
    * @return fitted model
    */
-  def fit(dataset: SchemaRDD, paramMap: ParamMap): M
+  def fit(dataset: DataFrame, paramMap: ParamMap): M
 
   /**
    * Fits multiple models to the input data with multiple sets of parameters.
@@ -61,7 +61,7 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params {
    * @param paramMaps an array of parameter maps
    * @return fitted models, matching the input parameter maps
    */
-  def fit(dataset: SchemaRDD, paramMaps: Array[ParamMap]): Seq[M] = {
+  def fit(dataset: DataFrame, paramMaps: Array[ParamMap]): Seq[M] = {
     paramMaps.map(fit(dataset, _))
   }
 }